For legal documents, embedding models that handle domain-specific language, long context, and precise semantic relationships are most effective. Legal texts often contain specialized terminology, complex sentence structures, and nuanced meanings, so generic models may struggle. The best choices are typically models pre-trained on legal corpora, models optimized for long documents, and those fine-tuned for tasks like contract analysis or case law retrieval. These embeddings should capture subtle differences between terms like “shall” versus “may” or recognize references to statutes, which are critical in legal contexts.
Domain-specific models like LEGAL-BERT or CaseLawBERT are strong candidates. LEGAL-BERT, for example, is a variant of BERT trained on legal texts such as court opinions and legislation. It better understands legal jargon and citation patterns compared to general-purpose BERT. Similarly, models like Lawformer are designed to process long legal documents by combining techniques for handling extended context. Legal documents often exceed the 512-token limit of standard transformers, so models with sparse attention mechanisms or hierarchical structures (e.g., splitting documents into sections) are practical. For example, a model might embed individual clauses of a contract separately before combining them, ensuring critical details aren’t lost.
Task-specific fine-tuning further improves performance. A model pre-trained on general legal data can be adapted for narrower use cases, such as identifying clauses related to liability in contracts or matching case law precedents. Sentence-transformers like all-mpnet-base-v2, when fine-tuned on legal sentence pairs (e.g., aligned questions and case summaries), excel at retrieval tasks. Hybrid approaches, such as combining embeddings with entity recognition (e.g., extracting parties, dates, or obligations first), also work well. Tools like spaCy’s legal NER models or Hugging Face’s AutoModelForTokenClassification can pre-process text before generating embeddings, adding structure to unstructured documents. For multilingual legal systems, models like XLM-R trained on legal corpora across languages (e.g., EU legislation in multiple languages) are valuable. The key is balancing domain specialization, context handling, and alignment with the end task—whether it’s search, classification, or summarization.